This study explores the effects of despotic leadership (DL) on employee job satisfaction (JS) using self-efficacy (SE) as a mediating variable and leader–member exchange (LMX) as a moderated variable. Building on the social learning and social exchange theory, our research proposes a research model. In this model, despotic leadership affects employee job satisfaction both directly and indirectly through self-efficacy and leader–member exchange. We used a questionnaire survey analysis approach to collect data. Data were collected from the employees of small- and medium-sized enterprises (SMEs) located in Guangdong Province, P.R. China. A pilot test of 20 participants with similar demographics as the final sample was performed to test the usability of the questionnaire. We distributed 500 questionnaires among the target population. In total, 230 usable questionnaires were returned, resulting in a response rate of 53%. To estimate the proposed relationships in the theoretical framework, we used SPSS and AMOS. The results of this study confirmed that despotic leadership has a negative impact on employee job satisfaction. Moreover, the outcomes of this study indicate that self-efficacy has a mediating effect between despotic leadership and employee job satisfaction. Similarly, the results also confirm that LMX has a moderating effect between despotic leadership and employee self-efficacy. Therefore, we conclude that the community is understanding of the mechanism of despotic leadership, identify the mechanism to effectively deal with its negative effects, broaden the relevant research on the antecedent variable of self-efficacy, and provide practical enlightenment enterprises to retain and employ people.

It has been contended that violence is prevalent in the workplace, and there has been increasing research interest into its potential effects. Human interactions at workplaces are apparent. However, the interactions among humans may have positive or negative dimensions. Usually, the positive or negative interactions between workers lead to different outcomes. Sometimes, they lead to a productive working environment; however, in some cases, they lead to toxicity among workers. In this study, we investigate the impact of workplace violence (WV) on innovative work behavior (IWB). Specifically, it examines the impact of the three dimensions of WV, namely, harassment, mobbing, and sabotage. Moreover, employees’ wellbeing mediates the relationship between WV (harassment, mobbing, and sabotage) and IWB. A questionnaire survey approach was used in this study. The target population were the workers of SMEs entrepreneurs located in Guangdong Province (China). The results confirm that, in the direct relationship, WV (harassment, mobbing, and sabotage) has a negative relationship with innovative IWB. Moreover, results also confirm that employee wellbeing is mediated between WV (harassment, mobbing, and sabotage) and IWB. Therefore, the empirical results of this paper identify that workplace violence reduces employees’ innovative work behavior by reducing their subjective and eudemonic wellbeing, which further broadens the perspective of IWB’s motivation analysis. Practical implications for small and medium enterprise organizations have also been discussed in this paper.

The prognostic value of albumin-to-alkaline phosphatase ratio (AAPR) in patients with hepatocellular carcinoma (HCC) remains controversial. This meta-analysis aims to evaluate the prognostic role of AAPR in patients with HCC. The databases of Web of Science, Embase, Cochrane Library and PubMed were comprehensively searched from inception to April 25, 2022. Pooled hazard ratio (HR) and 95% confidence interval (CI) were calculated with Stata 16.0 software for the assessment of the relationship between AAPR and overall survival (OS) as well as recurrence-free survival (RFS) in patients with HCC. A total of 2634 patients from 12 cohorts were included in this meta-analysis. The pooled results showed that lower AAPR predicted poorer OS (HR 2.02, 95% CI 1.78–2.30). Similarly, pooled results demonstrated that lower AAPR also predicted poorer RFS (HR 1.88, 95% CI 1.37–2.57). The heterogeneity for RFS by multivariate analytic results and the publication bias for OS existed, however, the subgroup analysis, meta-regression analysis as well as adjustment using trim-and-fill analysis confirmed an association between AAPR and OS as well as RFS. This meta-analysis proves that lower AAPR in patients with HCC predicted inferior survival outcomes, and AAPR might be a promising indicator for the prognosis of HCC.

With the development of digital health, enhancing decision-making effectiveness has become a critical task. This study proposes an improved Artificial Bee Colony (ABC) algorithm aimed at optimizing decision-making models in the field of digital health. The algorithm draws inspiration from the dual-layer evolutionary space of cultural algorithms, combining normative knowledge from the credibility space to dynamically adjust the search range, thereby improving both convergence speed and exploration capabilities. Additionally, a population dispersion strategy is introduced to maintain diversity, effectively balancing global exploration with local exploitation. Experimental results show that the improved ABC algorithm exhibits a 96% convergence probability when approaching the global optimal solution, significantly enhancing the efficiency and accuracy of medical resource optimization, particularly in complex decision-making environments. Integrating this algorithm with the Chat Generative Pre-trained Transformer (ChatGPT) decision system can intelligently generate personalized decision recommendations and leverage natural language processing technologies to better understand and respond to user needs. This study provides an effective tool for scientific decision-making in digital healthcare and offers critical technical support for processing and analyzing large-scale medical data.